Edge detection based on background change

Abstract

A method detects edges of an object in a scene by first acquiring a static image of the scene when the scene is static, and a live image of the scene including the object. A reference image is constructed from the static image. Then, image gradients of the reference image are compared with image gradients of the live image to identify edges of the object in the live image.

1. A method for detecting edges for an object in a scene, comprising:
 acquiring a static image of the scene when the scene is static; 
 acquiring a live image of the scene including the object; 
 constructing a reference image from the static image, and for each pixel in the live image: 
 (a) testing if a gradient magnitude is greater than a first threshold and continuing to (b) if true, else labeling the pixel as not an object edge; 
 (b) testing if the gradient magnitude is a local maximum and continuing to (c) if true, else labeling the pixel as not an object edge; 
 (c) testing if a distance between the gradient magnitude at the pixel and a corresponding gradient magnitude of a corresponding pixel in the reference image is less than a second threshold and continuing to (d) if true, else labeling the pixel as an object edge; 
 (d) testing if a distance between the gradient direction at the pixel and a corresponding gradient direction of a corresponding pixel in the reference image is less than a third threshold and continuing to (e) if true, else labeling the pixel as an object edge; 
 (e) testing if a distance between an intensity at the pixel and a corresponding intensity of a neighboring pixel in a gradient direction of lower intensity is less than a fourth threshold and continuing to (f) if true, else labeling the pixel as an object edge; and 
 (f) testing if a distance between the intensity at the pixel and a corresponding intensity of a neighboring pixel in a gradient direction of higher intensity is less than a fifth threshold and labeling the pixel as not a object edge if true, else labeling the pixel as an object edge. 
 
   
   
     2. The method of  claim 1  further comprising:
 linking the edges of the object to form edge chains. 
 
   
   
     3. The method of  claim 1  further comprising:
 measuring a distance between the image gradient of each pixel in the reference image and the image gradient of the corresponding pixel in the live image to identify the edges of the object. 
 
   
   
     4. The method of  claim 3  wherein the distance measurement is statistical. 
   
   
     5. The method of  claim 4  wherein the reference image is constructed from multiple static images, and further comprising:
 determining mean and variance values for the image gradients, and wherein the distance measurement is a Mahalanobis distance. 
 
   
   
     6. The method of  claim 1  wherein the edges of the object are detected in stereo images to provide a 3D reconstruction for the object. 
   
   
     7. The method of  claim 6  further comprising:
 analyzing the 3D reconstruction to determine whether the object is a wheelchair user or an ambulatory person. 
 
   
   
     8. The method of  claim 7  further comprising:
 measuring a height of the 3D reconstruction to determine whether the object is a wheelchair user. 
 
   
   
     9. The method of  claim 7  further comprising:
 measuring a shape of the 3D reconstruction to determine whether the object is a wheelchair user. 
 
   
   
     10. The method of  claim 7  further comprising:
 measuring a rigidity at the base of the 3D reconstruction to determine whether the object is a wheelchair user. 
 
   
   
     11. The method of  claim 7  wherein a signal is generated if the object is the wheelchair user. 
   
   
     12. The method of  claim 6  wherein there are multiple objects in the scene, further comprising:
 segmenting the 3D reconstruction into an individual 3D reconstruction for each object in a scene.